Seasonal modes of dryness and wetness variability over Europe and their connections with large scale atmospheric circulation and global sea surface temperature

Ionita, Mónica; Boroneanţ, C.; Chelcea, Silvia

doi:10.1007/s00382-015-2508-2

Cited by 61 publications

(52 citation statements)

References 65 publications

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“…Their work highlighted the challenges encountered in Europe using climate indices for seasonal streamflow forecasting. In Europe, teleconnections show complex patterns and a strong seasonal dependence (Ionita et al, 2015). Some studies have thus proposed conditioning past precipitation or streamflow scenarios based on previous amounts of precipitation or on previous streamflow anomalies (Sauquet et al, 2008;Svensson, 2016).…”

Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Crochemore¹,

Ramos²,

Pappenberger³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in longrange streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

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Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Crochemore¹,

Ramos²,

Pappenberger³

et al. 2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Moreover, as temperature extremes are usually closely linked with specific circulation patterns, such as atmospheric blockings (Rimbu et al 2014;Ionita et al 2015), studies of long-term extreme variability can also be helpful when assessing the history of certain type of atmospheric circulation patterns in a region.…”

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confidence: 99%

Can tree-ring density data reflect summer temperature extremes and associated circulation patterns over Fennoscandia?

et al. 2016

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MXD chronologies over the period 1901-1978, 48% of the total variance of the summer warm-day extreme variability over the period , and 36% of the total variance in the frequency of a summer anticyclonic pattern centered over eastern-central Fennoscandia in the period 1948-2006. The frequency of summer warm-day extremes in Fennoscandia is likely linked to a meridional shift of the northern mid-latitude jet stream. This study shows that the MXD network can be used to infer the variability of past summer warm-day extremes and the frequency of the associated summer anticyclonic circulation pattern over Fennoscandia.

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“…For extended and more detailed studies of various circulation modes in Euro-Atlantic sector (or global modes of variability) consider reading e.g. Barnston and Livezey [1987]; Beranová and Huth [2008]; ; Ionita et al [2015].…”

Section: Other Modes Of Variability Over Europementioning

confidence: 99%

Spatial and temporal scales of atmospheric dynamics

Jajcay¹

2018

Preprint

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Earth climate, in general, varies on many temporal and spatial scales. In particular, climate observables exhibit recurring patterns and quasi-oscillatory phenomena with different periods. Although these oscillations might be weak in amplitude, they might have a non-negligible influence on variability on shorter time-scales due to cross-scale interactions, recently observed by Paluš [2014b]. This thesis supplies an introductory material for inferring the cross-scale information transfer from observational data, where the time series of interest are obtained using wavelet transform, and possible information transfer is studied using the tools from information theory. Finally, crossscale interactions are studied in two climate phenomena: air temperature variability in Europe, in which we study phase-amplitude coupling from a slower oscillatory mode with an 8-year period on faster variability and its effects, and El Niño/ Southern Oscillation where we observe a causal chain of phase-phase and phase-amplitude couplings among distinct oscillatory modes.

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Seasonal modes of dryness and wetness variability over Europe and their connections with large scale atmospheric circulation and global sea surface temperature

Cited by 61 publications

References 65 publications

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Can tree-ring density data reflect summer temperature extremes and associated circulation patterns over Fennoscandia?

Spatial and temporal scales of atmospheric dynamics

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